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Categorizing Wetland Vegetation by Airborne Laser Scanning on Lake Balaton and Kis-Balaton, Hungary

Outlining patches dominated by different plants in wetland vegetation provides information on species succession, microhabitat patterns, wetland health and ecosystem services. Aerial photogrammetry and hyperspectral imaging are the usual data acquisition methods but the application of airborne laser scanning (ALS) as a standalone tool also holds promises for this field since it can be used to quantify 3-dimensional vegetation structure. Lake Balaton is a large shallow lake in western Hungary with shore wetlands that have been in decline since the 1970s. In August 2010, an ALS survey of the shores of Lake Balaton was completed with 1 pt/m2 discrete echo recording. The resulting ALS dataset was processed to several output rasters describing vegetation and terrain properties, creating a sufficient number of independent variables for each raster cell to allow for basic multivariate classification. An expert-generated decision tree algorithm was applied to outline wetland areas, and within these, patches dominated by Typha sp. Carex sp., and Phragmites australis. Reed health was mapped into four categories: healthy, stressed, ruderal and die-back. The output map was tested against a set of 775 geo-tagged ground photographs and had a user’s accuracy of > 97% for detecting non-wetland features (trees, artificial surfaces and low density Scirpus stands), > 72% for dominant genus detection and > 80% for most reed health categories (with 62% for one category). Overall classification accuracy was 82.5%, Cohen’s Kappa 0.80, which is similar to some hyperspectral or multispectral-ALS fusion studies. Compared to hyperspectral imaging, the processing chain of ALS can be automated in a similar way but relies directly on differences in vegetation structure and actively sensed reflectance and is thus probably more robust. The data acquisition parameters are similar to the national surveys of several European countries, suggesting that these existing datasets could be used for vegetation mapping and monitoring.

Detection of fallen trees in forested areas using small footprint airborne laser scanning data

Deadwood was identified as an important indicator for habitat condition and biodiversity in forests. The assessment of downed trees is therefore part of sustainable forest management and ecological monitoring. However, manual quantification of deadwood in forests is challenging, time consuming, and considered cost-inefficient. Full-waveform airborne laser scanning (FWF-ALS) can be used to support the assessment process. The amplitude and width of the backscattered pulses contain information on the properties of the surface. We used these observations for the identification of downed trees in a Natura2000 forest site. A high density FWF-ALS data set was acquired under leaf-off conditions. Echo width and type (i.e., first, intermediate, and last) information as well as normalized echo heights were used to filter the point cloud and derive a digital height model (DHM). This DHM depicts downed stems as line-like features. Image processing was applied to derive and refine regions representing fallen trees. Terrestrial reference data consisting of locations and dimensions of downed trees, as well as state of decay were used for evaluation. Direct identification of downed trees in FWF-ALS point clouds was possible (completeness 75%, correctness 90%), but it was influenced by factors such as dimension, state of decay, vegetation density, and penetration of the laser.

Categorizing Grassland Vegetation with Full-Waveform Airborne Laser Scanning: A Feasibility Study for Detecting Natura 2000 Habitat Types

There is increasing demand for reliable, high-resolution vegetation maps covering large areas. Airborne laser scanning data is available for large areas with high resolution and supports automatic processing, therefore, it is well suited for habitat mapping. Lowland hay meadows are widespread habitat types in European grasslands, and also have one of the highest species richness. The objective of this study was to test the applicability of airborne laser scanning for vegetation mapping of different grasslands, including the Natura 2000 habitat type lowland hay meadows. Full waveform leaf-on and leaf-off point clouds were collected from a Natura 2000 site in Sopron, Hungary, covering several grasslands. The LIDAR data were processed to a set of rasters representing point attributes including reflectance, echo width, vegetation height, canopy openness, and surface roughness measures, and these were fused to a multi-band pseudo-image. Random forest machine learning was used for classifying this dataset. Habitat type, dominant plant species and other features of interest were noted in a set of 140 field plots. Two sets of categories were used: five classes focusing on meadow identification and the location of lowland hay meadows, and 10 classes, including eight different grassland vegetation categories. For five classes, an overall accuracy of 75% was reached, for 10 classes, this was 68%. The method delivers unprecedented fine resolution vegetation maps for management and ecological research. We conclude that high-resolution full-waveform LIDAR data can be used to detect grassland vegetation classes relevant for Natura 2000.

Boosting the predictive accuracy of urban hedonic house price models through airborne laser scanning

This paper introduces an integrative approach to hedonic house price modeling which utilizes high density 3D airborne laser scanning (ALS) data. In general, it is shown that extracting exploratory variables using 3D analysis – thus explicitly considering high-rise buildings, shadowing effects, etc. – is crucial in complex urban environments and is limited in well-established raster-based modeling. This is fundamental in large-scale urban analyses where essential determinants influencing real estate prices are constantly missing and are not accessible in official and mass appraiser databases. More specifically, the advantages of this methodology are demonstrated by means of a novel and economically important externality, namely incoming solar radiation, derived separately for each flat. Findings from an empirical case study in Vienna, Austria, applying a non-linear generalized additive hedonic model, suggest that solar radiation is significantly capitalized in flat prices. A model comparison clearly proves that the hedonic model accounting for ALS-based solar radiation performs significantly superior. Compared to a model without this externality, it increases the model’s explanatory power by approximately 13% and additionally reduces the prediction error by around 15%. The results provide strong evidence that explanatory variables originating from ALS, explicitly regarding the immediate 3D surroundings, enhance traditional hedonic models in urban environments.

A voxel-based approach for canopy structure characterization using full-waveform airborne laser scanning

Forests play a significant role in the global biogeochemical and -physical cycles and particularly the complex three-dimensional forest canopy structure influences the fluxes of energy and matter between the atmosphere and forests. Assessing this structure quantitatively using conventional fieldwork or traditional remote sensing methods is difficult, whereas airborne laser scanning (ALS) systems have proven to be suitable for providing explicit vertical information for large areas. However, most existing ALS based approaches include manual processing steps or need additional data about stand characteristics. To solve these issues, a robust and automatic multi-dimensional clustering method was developed to derive forest canopy structure types (CSTs) based on full-waveform ALS data. The results show that it is possible to develop an automatic, self-sustained and transferable method for: the extraction of CSTs without any previous knowledge about the forest stand; and the extraction of bio-physical parameters based on the resulting CSTs.

Full-Waveform Airborne Laser Scanning Systems and Their Possibilities in Forest Applications

Full-waveform (FWF) airborne laser scanning (ALS) systems became available for operational data acquisition around the year 2004. These systems typically digitize the analogue backscattered echo of the emitted laser pulse with a high frequency. FWF digitization has the advantage of not limiting the number of echoes that are recorded for each individual emitted laser pulse. Studies utilizing FWF data have shown that more echoes are provided from reflections in the vegetation in comparison to discrete echo systems. To obtain geophysical metrics based on ALS data that are independent of a mission’s flying height, acquisition time or sensor characteristics, the FWF amplitude values can be calibrated, which is an important requirement before using them in further classification tasks. Beyond that, waveform digitization provides an additional observable which can be exploited in forestry, namely the width of the backscattered pulse (i.e. echo width). An early application of FWF ALS was to improve ground and shrub echo identification below the forest canopy for the improvement of terrain modelling, which can be achieved using the discriminative capability of the amplitude and echo width in classification algorithms. Further studies indicate that accuracies can be increased for classification (e.g. species) and biophysical parameter extraction (e.g. diameter at breast height) for single-tree- and area-based methods by exploiting the FWF observables amplitude and echo width.

Operational forest structure monitoring using airborne laser scanning Flugzeuggestütztes Laserscanning für ein operationelles Waldstrukturmonitoring

Die Struktur des Waldes hat einen signifikanten Einfluss auf die globalen bio- geochemischen Stoffkreislaufe und kann daruber hinaus als Indikator dienen, um das Potential zum Erhalt der Biodiversitat abzuschatzen und die Widerstandsfahigkeit des Waldes gegen ausere Einflusse zu bestimmen. Flugzeuggestutztes Laserscanning (ALS) bietet hierbei die Moglichkeit einer raumlich hochaufgelosten Erfassung und Beschreibung sowohl der horizontalen als auch der vertikalen Waldstruktur. Wir stellen robuste Verfahren basierend auf flugzeuggestutzten Laserscanningdaten vor, um eine Extraktion von forstwirtschaftlich und -wissenschaftlich relevanten Strukturinformationen zu ermoglichen. Dies beinhaltet: i) die Einzelbaumextraktion, ii) die Bestimmung von Unterwuchs und Bodenbedeckung und iii) die Totholzerkennung. Die Datengrundlage bestand aus multi-temporalen, full-waveform Laserdaten in dichtem Laub- und Mischwald fur Testgebiete in der Schweiz (Lagern) und in Deutschland (Uckermark). Basierend auf der ALS-Punktwolke mit ihren geometrischen Attributen und den zugehorigen full-waveform Eigenschaften wurden folgende Methoden angewendet: i) hierarchisches, 3D-Clustering und die Ableitung von alpha shapes fur die Einzelbaumextraktion, ii) rasterbasierte, vertikale Stratifizierung fur die Charakterisierung von Unterwuchs, und iii) die Kombination aus map algebra und Vektorisierung fur die Totholzanalyse. Die erzielten Genauigkeiten der abgeleiteten Strukturvariablen entsprachen den Anforderungen der traditionellen Forstinventur. Vorbehaltlich der Verfugbarkeit einer entsprechenden Datengrundlage (multi-temporale ALS-Daten mit hohen Punktdichten) ist es mit den vorgestellten robusten Methoden moglich, ein grosflachiges und operationelles Waldstrukturmonitoring durchzufuhren.